Arguments

data

data.frame that has columns named "period" and "outcome" (period
is the time period that the outcome action was taken), and the rest of the
columns are predictors, ranging from one to three predictors. All of the
(3-5 columns) should be named. The period and outcome columns should be
integer vectors and the columns with the predictor variable data should be
logical vectors
(TRUE, FALSE).
If the predictor variable data is not
logical, it will coerced to logical withbase::as.logical().

test_data

Optional data.frame that has "period" and "outcome" columns
and rest of columns are predictors, ranging from one to three predictors.
All of the (3-5 columns) should be named. Outcome variable is the decision
the decision-maker took for that period. This data.frame should be in the
same format and have the same order of columns as the data.frame passed to
the required data argument.

drop_nzv

Optional logical vector length one specifying whether
predictors variables with variance in provided data near zero should be
dropped before model building. Default is FALSE. See
caret::nearZeroVar(), which calls:caret::nzv().

measure

Optional length one character vector that is either:
"accuracy", "sens", "spec", or "ppv". This specifies what measure of
predictive performance to use for training and evaluating the model. The
default measure is "accuracy". However, accuracy can be a problematic
measure when the classes are imbalanced in the samples, i.e. if a class the
model is trying to predict is very rare. Alternatives to accuracy are
available that illuminate different aspects of predictive power. Sensitivity
answers the question, “ given that a result is truly an event, what is the
probability that the model will predict an event?” Specificity answers the
question, “given that a result is truly not an event, what is the
probability that the model will predict a negative?” Positive predictive
value answers, “what is the percent of predicted positives that are
actually positive?”

states

Optional numeric vector with the number of states.
If not provided, will be set to max(data$outcome).

cv

Optional logical vector length one for whether cross-validation
should be conducted on training data to select optimal number of states.
This can drastically increase computation time because if TRUE, it
will run evolve_model k*max_states times to estimate optimal value for
states. Ties are broken by choosing the smaller number of states. Default is
FALSE.

max_states

Optional numeric vector length one only relevant if
cv==TRUE. It specifies how up to how many states that
cross-validation should search through.
If not provided, will be set to states + 1.

k

Optional numeric vector length one only relevant if cv==TRUE,
specifying number of folds for cross-validation.

actions

Optional numeric vector with the number of actions. If not
provided, then actions will be set as the number of unique values in the
outcome vector.

seed

Optional numeric vector length one.

popSize

Optional numeric vector length one specifying the size of the
GA population. A larger number will increase the probability of finding a
very good solution but will also increase the computation time. This is
passed to the GA::ga() function of the GA package.

pcrossover

Optional numeric vector length one specifying probability of
crossover for GA. This is passed to the GA::ga() function of the GA
package.

pmutation

Optional numeric vector length one specifying probability of
mutation for GA. This is passed to the GA::ga() function of the GA
package.

maxiter

Optional numeric vector length one specifying max number of
iterations for stopping the GA evolution. A larger number will increase the
probability of finding a very good solution but will also increase the
computation time. This is passed to the GA::ga() function of the GA
package. maxiter is scaled by how many parameters are in the model:maxiter <- maxiter + ((maxiter*(nBits^2)) / maxiter).

run

Optional numeric vector length one specifying max number of
consecutive iterations without improvement in best fitness score for
stopping the GA evolution. A larger number will increase the probability of
finding a very good solution but will also increase the computation time.
This is passed to the GA::ga() function of the GA package.

parallel

Optional logical vector length one. For running the GA
evolution in parallel. Depending on the number of cores registered and the
memory on your machine, this can make the process much faster, but only works
for Unix-based machines that can fork the processes.

priors

Optional numeric matrix of solutions strings to be included in
the initialization. User needs to use a decoder function to translate prior
decision models into bits and then provide them. If this is not specified,
then random priors are automatically created.

verbose

Optional logical vector length one specifying whether helpful
messages should be displayed on the user's console or not.

return_best

Optional logical vector length one specifying whether to
return just the best model or all models. Only relevant if ntimes > 1.
Default is TRUE.

ntimes

Optional integer vector length one specifying the number of
times to estimate model. Default is 1 time.

Details

This is the main function of the datafsm package. It relies on the
GA package for genetic algorithm optimization. evolve_model
takes data on predictors and data on the outcome. It automatically creates a
fitness function that takes the data, an action vector evolve_model
generates, and a state matrix evolve_model generates as input and
returns numeric vector of the same length as the outcome.
evolve_model then computes a fitness score for that potential solution
FSM by comparing it to the provided outcome. This is repeated for every
FSM in the population and then the probability of selection for the next
generation is proportional to the fitness scores. The default is also for the
function to call itself recursively while varying the number of states inside
a cross-validation loop in order to estimate the optimal number of states.

If parallel is set to TRUE, then these evaluations are distributed across the
available processors of the computer using the doParallel package,
otherwise, the evaluations of fitness are conducted sequentially. Because
this fitness function that evolve_model creates must loop through all
the data every time it is evaluated and we need to evaluate many possible
solution FSMs, the fitness function is implemented in C++ so it is very fast.

evolve_model uses a stochastic meta-heuristic optimization routine to
estimate the parameters that define a FSM model. Generalized simulated
annealing, or tabu search could work, but they are more difficult to
parallelize. The current version uses the GA package's genetic
algorithm because GAs perform well in rugged search spaces to solve integer
optimization problems, are a natural complement to our binary string
representation of FSMs, and are easily parallelized.

This function evolves the models on training data and then, if a test set is
provided, uses the best solution to make predictions on test data. Finally,
the function returns the GA object and the decoded version of the best string
in the population. See ga_fsm for the details of the slots
(objects) that this type of object will have.

Value

Returns an S4 object of class ga_fsm. See ga_fsm for the
details of the slots (objects) that this type of object will have and for
information on the methods that can be used to summarize the calling and
execution of evolve_model(), including summary, print,
and plot. Timing measurement is in seconds.